Timeline for Power analysis for t-test instead of ANOVA
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 15, 2019 at 17:20 | comment | added | Izy | Also, I'm not sure how you plan to carry out the power analysis, but I might use something like this in R: power.t.test(delta=0.2,sd=0.1,sig.level=0.05,power=0.8,type="paired"). Note that you need to include an estimate of the standard deviation (the default in R is sd=1, if you don't set it you're just assuming that sd=1). Do you think that the change in factor B might increase the variance of your differences for A1-A2? If it might do, and you fail to account for that when you estimate the sd, then it is very likely that you will underestimate the required sample size. | |
| Feb 15, 2019 at 17:13 | comment | added | Izy | Please can you provide a dummy ANOVA results table showing the degrees of freedom... including how many residual degrees of freedom you will have? I think that might answer your own question, as I think the individual paired t-tests will overestimate the residual degrees of freedom for a given sample size (therefore giving you an underestimate of the required sample size). | |
| Feb 15, 2019 at 15:02 | comment | added | YBA | Thanks for the answer. I am still interested in the answer to my specific question. Does anyone know whether there is any risk of underestimation of the sample size? | |
| Feb 15, 2019 at 10:43 | history | answered | Izy | CC BY-SA 4.0 |